• Proceedings of the Computational Structural Engineering Institute Conference
• /
• 1995.04a
• /
• pp.136-143
• /
• 1995

The problem of the structural identification becomes important, particularly with relation to the rapid increase of the number of the damaged or deteriorated structures, such as highway bridges, buildings, and industrial facilities. This paper summarizes the recent studies related to those problems by the present authors. The system identfication methods are generally classified as the time domain and the frequency domain methods. As time doamin methods, the sequential algorithms such as the extended Kalman filter and the sequential prediction error method are studied. Several techniques for improving the convergences are incorporated. As frequency domain methods, a new frequency response function estimator is introduced. For damage estimation of existing structures, the modal perturbation and the sensitivity matrix methods are studied. From the example analysis, it has been found that the combined utilization of the measurement data for the static response and the dynamic (modal) properties are very effictive for the damage estimation.

• The Transactions of the Korean Institute of Electrical Engineers
• /
• v.34 no.1
• /
• pp.1-9
• /
• 1985

In this study we described Binary-Decision Method Programmable Controller that is faster than conventional Programmable Controller (P.C) using the Boolean method. An algorithm for the optimal BD program was systematically developed by using the incomplete multiple output function and the residual characteristic function. The potential applications of BD programmable controller for the problems of industrial control were illustrated, which include on-off sequential control function and also the tasks involving continuous data processing and PID control. An example was presented that has shown how BD programmable controller and ICU (Industrial Control Unit) controller can be applied to the sequential logics.

• Proceedings of the IEEK Conference
• /
• 1998.10a
• /
• pp.955-958
• /
• 1998

This paper describes the algorithm which extracts moving vehicles from sequential images and tracks those vehicles using Kalman filter. This work is composed of a motion segmentation stage which extracts moving objects from sequential images and gets features of objects, and a motion estimation stage which estimates the position and the motion of moving objects using Kalman filter. In the motion estimation stage, applying to affine motion model we divided the Kalman filter into position filter and velocity filter to employ linear Kalman filter. Multi-target tracking requires a data association component that decides which measurement to use for updating the state of which object. We use pattern recognition method to solve this problem.

• Proceedings of the KSRS Conference
• /
• 2003.11a
• /
• pp.1483-1485
• /
• 2003

Multitemporal approaches using sequential data acquired over multiple years are essential for satisfactory discrimination between many land cover classes whose signatures exhibit seasonal trends. At any particular time, the response of several classes may be indistinguishable. Using the estimates of periodogram which are obtained from sequential images, the periodicity of the process have been incorporates into multitemporal classification. The Normalized Difference Vegetation Index (NDVI) was computed for seven-day composites of the Advanced Very High Resolution Radiometer (AVHRR) imagery over the Korean peninsula for 1996 - 2000 using a dynamic technique.

• Transactions of Materials Processing
• /
• v.20 no.3
• /
• pp.206-213
• /
• 2011

A flexible die, which is composed of a number of punches with adjusted heights to form a three-dimensional curved surface, is a crucial part of a flexible forming technology. In this study, the punch and control system of the flexible die were designed. The flexible die is divided into three modules, namely, punch, control and joint, and the corresponding modules were developed. The punch module materializes a three-dimensional forming surface by the control module, which is composed of an AC servo motor set and a linear guide. The joint module is necessary for the sequential motion between the servo motor set and the punch module. A sequential motion algorithm for the AC servo motor set, that uses the data of the punch relative heights, was also proposed. Finally, a flexible stretch forming test was carried out using the presently designed flexible die.

• Proceedings of the KIEE Conference
• /
• 1998.07b
• /
• pp.657-659
• /
• 1998

In this paper we discuss an algorithm to discriminate a target under track against multiple acoustic counter-measure (ACM) sources, based on sequential testings of multiple hypotheses. The ACM sources are separated from the target under track and generate, while drifting, measurements with false range and Doppler information. The purpose of the ACM is to mislead the target tracking and to help the true target evade a pursuer. The proposed algorithm uses as a test statistic a function of both the sequences of processed waveform signature and the innovation sequences from extended Kalman filters to estimate the target dynamics and the drifting positions of the ACM sources. Numerical experiments on various scenarios show that the proposed algorithm discriminates the target faster with a higher probability of success than the algorithm using only the innovation sequences from extended Kalman filters.

• Journal of Korea Multimedia Society
• /
• v.14 no.12
• /
• pp.1544-1548
• /
• 2011

A process of choosing a subset of original features, so called feature selection, is considered as a crucial preprocessing step to image processing applications. There are already large pools of techniques developed for machine learning and data mining fields. In this paper, basically two methods, non-feature selection and feature selection, are investigated to compare their predictive effectiveness of classification. Color co-occurrence feature is used for defining image features. Standard Sequential Forward Selection algorithm are used for feature selection to identify relevant features and redundancy among relevant features. Four color spaces, RGB, YCbCr, HSV, and Gaussian space are considered for computing color co-occurrence features. Gray-level image feature is also considered for the performance comparison reasons. The experimental results are presented.

• Proceedings of the KIEE Conference
• /
• 2006.10c
• /
• pp.434-436
• /
• 2006

In a visual driver-assistance system, separating moving objects from fixed objects are an important problem to maintain multiple hypothesis for the state. Color and edge-based tracker can often be "distracted" causing them to track the wrong object. Many researchers have dealt with this problem by using multiple features, as it is unlikely that all will be distracted at the same time. In this paper, we improve the accuracy and robustness of real-time tracking by combining a color histogram feature with a brightness of Optical Flow-based feature under a Sequential Monte Carlo framework. And it is also excepted from Tracking as time goes on, reducing density by Adaptive Particles Number in case of the fixed object. This new framework makes two main contributions. The one is about the prediction framework which separating moving objects from fixed objects and the other is about measurement framework to get a information from the visual data under a partial occlusion.

• Communications for Statistical Applications and Methods
• /
• v.8 no.3
• /
• pp.777-786
• /
• 2001

The autoregressive models have been used to describe a wade variety of time series. Then the problem of determining the order in the times series model is very important in data analysis. We consider the Bayesian approach for finding the order of autoregressive(AR) error models using the latent variable which is motivated by Tanner and Wong(1987). The latent variables are combined with the coefficient parameters and the sequential steps are proposed to set up the prior of the latent variables. Markov chain Monte Carlo method(Gibbs sampler and Metropolis-Hasting algorithm) is used in order to overcome the difficulties of Bayesian computations. Three examples including AR(3) error model are presented to illustrate our proposed methodology.

• Communications for Statistical Applications and Methods
• /
• v.6 no.2
• /
• pp.543-564
• /
• 1999

The principal aim of a sequential phase I clinical trial in which the toxicity reponses of a group of patient(s) determine the dose level of the next patient(s) group is to estimate the maximal tolerated dose(MTD) of a new drug, In this paper we compared with a simulation study the performance of the MTD estimates that are determined by a stopping rule in a design and also those that are determined by analyzing the data after a clinical trial is terminated. To the latter belong the mean median mode and maximum likelihood estimates. For the Standard Methods the stopping rule MTD is quite inefficient but the median MTD has a best efficiency and is robust with respect to the three different toxicity curves. The problem of non-convergence of MLE MTD is severe. A more improved MTD estimate is produced by combining the advantages of the various MTD estimates and its efficiency is better than the single median MTD estimate especially for the toxicity curve of an unlucky choice of dose levels. The simulation results suggest that simple types of phase I designs can be combined with relatively standard analytic techniques to provide a more efficient MTD estimate.